Applied Logistic Regression Analysis

by Scott Menard

Overview

The focus in this Second Edition is again on logistic regression models for individual level data, but aggregate or grouped data are also considered. The book includes detailed discussions of goodness of fit, indices of predictive efficiency, and standardized logistic regression coefficients, and examples using SAS and SPSS are included.

• More detailed consideration of grouped as opposed to case-wise data throughout the book
• Updated discussion of the properties and appropriate use of goodness of fit measures, R-square analogues, and indices of predictive efficiency
• Discussion of the misuse of odds ratios to represent risk ratios, and of over-dispersion and under-dispersion for grouped data

Updated coverage of unordered and ordered polytomous logistic regression models. 

Product Details

ISBN-13:	9781544332581
Publisher:	SAGE Publications
Publication date:	10/09/2001
Series:	Quantitative Applications in the Social Sciences , #106
Sold by:	Barnes & Noble
Format:	eBook
Pages:	128
File size:	4 MB

About the Author

Scott Menard is a Professor of Criminal Justice at Sam Houston State University and a research associate in the Institute of Behavioral Science at the University of Colorado, Boulder. He received his A.B. at Cornell University and his Ph.D. at the University of Colorado, Boulder, both in Sociology. His interests include quantitative methods and statistics, life course criminology, substance abuse, and criminal victimization. His publications include Longitudinal Research (second edition Sage 2002), Applied Logistic Regression Analysis (second edition Sage 2002), Good Kids from Bad Neighborhoods (Cambridge University Press 2006, with Delbert S. Elliott, Bruce Rankin, Amanda Elliott, William Julius Wilson, and David Huizinga), Youth Gangs (Charles C. Thomas 2006, with Robert J. Franzese and Herbert C. Covey), and the Handbook of Longitudinal Research (Elsevier 2008), as well as other books and journal articles in the areas of criminology, delinquency, population studies, and statistics.

Table of Contents

Series Editor′s Introduction
Author′s Introduction to the Second Edition
1. Linear Regression and Logistic Regression Model
2. Summary Statistics for Evaluating the Logistic Regression Model
3. Interpreting the Logistic Regression Coefficients
4. An Introduction to Logistic Regression Diagnosis
Ch 5. Polytomous Logistic Regression and Alternatives to Logistic Regression
6. Notes
Appendix A
References
Tables
Figures